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Ask HN: Recommend me a course on Coursera
603 points by Eugeleo on April 9, 2020 | hide | past | favorite | 135 comments
My university just provided us with free Coursera accounts until the end of summer. However, there's so many courses to choose from that I don't know where to start! Please recommend me a course that you liked, preferably from the following areas:

- UX design

- bioinformatics

- statistics for data science

- mathematical analysis

- algebra or category theory

But of course, you don't need to stick to those categories, I'd love to learn about anything new!

Build a Modern Computer from First Principles: From Nand to Tetris (Project-Centered Course) https://www.coursera.org/learn/build-a-computer

and then part 2: https://www.coursera.org/learn/nand2tetris2

[Both courses are free]

These are fantastic courses, by far the best MOOCs I have ever taken. I went into them knowing nothing about computer architecture, and by the end of the first course I was able to design a fully-working digital computer in Logisim.

While other courses consist of lectures + text content, with Nand2Tetris the course is practical. The authors have developed a complete software system to allow you to complete the course:

* A simplified hardware programming language to design the ALU, CPU, clock, RAM, etc..

* A hardware simulator and debugger to allow you to test the hardware that you develop

* An assembler for the assembly programs you write for the computer

* A compiler for the higher-level programs you write for the computer

I'm probably banging-on about this course more than I reasonably should, but that's just because I enjoyed the course so much!

I have to also mention nandgame.com here. Similar idea, but all presented as an interactive, browser-based game. Start with nand gates and build up to a functioning computer. You never get to tetris, but it's great and super easy to start playing/learning. Delightful.

http://nandgame.com/ You‘re welcome.

Oh man, a big Thank You. I kind of know where I'll be spending 10-12 hours in the next few days.

Oh, I was hoping the development of an assembler and a compiler was a part of the course.

How useful would that course be for someone who knows about computer architecture, has coded in assembly, understands how compilers work, etc...?

Alternatively, would this course be useful/accessible for complete beginners, e.g. mid/high schoolers?

Here is a list of all Coursera courses sorted by ratings: https://www.classcentral.com/provider/coursera?sort=rating-u...

You can also filter by subjects i.e Computer Science, Data Science. Humanities, Mathematics, etc.

Disclaimer: I am the founder.

Is there a plan to get more advanced courses?

I'm asking because there seems to be an extreme bias towards beginners courses, or content that is rather limited in breadth and depth compared to what a university might teach during a full masters degree.

ie, there's about 50 security intro courses (with lots of overlap of course), one "advanced" course that's been delayed for long and isn't all that advanced (Crypto II from Stanford), but nothing that even comes close to the various full-semester courses covering particular niches that I took in university (for example, we did one full semester course on each of: symmetric crypto, asymmetric crypto, side channels, "special topics" (random stuff), a cryptoanalysis lab, and 3 more niche things - and those are just the pure crypto courses, and even/especially within that area I feel I've barely scratched the surface).

These university courses cover not only more topics than Coursera covers (overall; there are many things even in this niche that Coursera has that we weren't taught, which is neat), but within each we went into considerable depth. In particular we tended to approach them from a rigorous mathematical perspective (number theory, linear algebra, statistics, proofs, etc). My worry here is that Coursera might be more geared towards people that don't need to learn the topics well enough to be actually able to use them professionally, let alone academically. ie, more like edutainment than education (no offense intended. I wasn't sure if I should include that sentence cause it might sound harsh, but I think it illustrates what I'm getting at).

We also didn't have courses that are blatant advertisements (#18568).

I don't want to put Coursera down (quite the opposite), I am genuinely interested in your answer - Is it just me not seeing everything available? Is the field I'm (slightly) knowledgeable about an outlier? Or am I missing the point of Coursera (maybe it's more focused on training industry professionals than academics than universities?) Or is it correct, and if so, is it intentional or unintentional? Is there a single field of study where Coursera could replace a university partly/largely/mostly/entirely? Will there be?

Your parent is the founder of classcentral.com, not Coursera.

If you want classes that are more advanced or that go into greater depth, I recommend the courses offered by edX or Stanford-online or MIT OpenCourseware. These are full-term for-credit courses with video lectures at top schools that you can 'audit' for free, though few or none will grade your homework or projects unless you pay full tuition. By contrast, 95% of Coursera or Udemy courses are much shorter and more introductory.

There should be less ratings of advanced materials, and there should be less coursework. Advanced study is the long tail of learning.

That should not affect the rating by much with an appropriate sorting. See: https://www.evanmiller.org/how-not-to-sort-by-average-rating...

I would expect people who take advanced courses to rate systematically differently from people who take beginner courses. For comparisons between similar courses it's probably fine, but would be hard to use the scores to compare the value of beginner courses to advanced courses.

Of course, even the definition of better here is so ill-defined it probably is of no practical significance.

If you want classes that are more advanced or that go into greater depth, I recommend those offered by edX or Stanford-online or MIT OpenCourseware. These are full-term for-credit courses with video lectures (MIT, less so) at top schools that you can 'audit' for free, though none will grade your homework or projects unless you pay full tuition. By contrast, 90% of Coursera or Udemy courses are much shorter and more introductory.

Oh, thanks so much for that! This link showed me I have interest for things I did not even know existed on the platform. I mean, Mountains 101, Poetry? Awesome!

I would personally recommend Learning how to Learn!


Many thanks! This is a gem. Within a few minutes, I've seen interesting courses that Google search never found for me.

The Science of Wellbeing[1] taught by Yale’s Dr. Laurie Santos lives up to the hype. It’s been discussed on HN a few times[2] which is how I stumbled upon it.

If you don’t mind my asking, did your school give you access to coursera to earn credit while the campus is shut down? Or is it just something interesting and fun for students who might be inclined to learn something new while they’re stuck at home? Either way, props to your school! And enjoy whatever classes you decide to take!

[1] https://www.coursera.org/learn/the-science-of-well-being

[2] https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu...

Thanks for the recommendation, sounds great!

As mentioned above, I think the credit is due to Coursera more than my university; either way, at least they've let me know that something like this is possible.

It's just for fun; most of our courses are now taught over Zoom or similar services, assignments are handled digitally and if it wasn't for the low-quality webcams, you'd almost forget something is out of the ordinary.

Coursera for Campus for the time being is free. You need to ask your school to apply: https://www.coursera.org/campus/

EdX also has something similar.

NYU's Tandon School Engineering is doing the same with edX (since it is part of the organization), but students won't earn credits.

I enjoyed taking Model Thinking: https://www.coursera.org/learn/model-thinking

It's designed to be a foundation course for subsequent social science classes, but I personally found the exposure to models from different fields of study to be quite insightful.

If you're interested, there's also a book by the professor on the same topic: https://www.goodreads.com/en/book/show/39088592-the-model-th...

Model Thinking is the best Coursera course that I've taken. The lessons have practically applied to many areas of my personal and professional life in a way that far exceeded my expectations.

Scott is one of the best teachers on the topic, and makes complex models simple and intuitive to understand.

It is a long course, but well worth it. Cannot recommend it enough.

Came here to say the same. Especially now that the world is struggling with SARS-CoV-2/COVID-19, understanding these different models helps a lot!

Last but not least, Scott E. Page is a great educator. Glad to hear there is a book from him now -- his papers were a good read as well during/after the course.

Agreed. I took Model Thinking years back, and it's probably the course that I most enjoyed (as in: just for it's own sake). I had no idea there was now also a book.

+1. One of the best courses I took in Coursera, especially because my background is in Engineering, not Social Sciences.

I took this course few years back as well. Quite enjoyed it at that time. Recommended!

I recommend the meta-cognition course: Learning how to Learn.


The primary instructor, Dr. Barbara Oakley, wrote the book, _A Mind for Numbers: How to Excel at Math and Science (Even If You Flunked Algebra)_ that isn't just about learning math.

I second this, and if you're not sure where to start then this is a great one because it will give you some study tools to use on your next course(s)!

I can second this too. The presentation was a bit crude sometimes but the course was good nevertheless.

I recommend an audiobook course from Audible called

The Philosophers Toolkit: How to be the most rational person in any room by The Great Courses https://www.audible.com/pd/The-Philosophers-Toolkit-How-to-B...

It teaches you mental models on how to think and find a solution to a problem. It explains the concepts behind each model quite well.

Topics include how to determine a valid argument, an iron clad argument, using heuristics to solve problems, among other things.

My only gripe with the course I linked is that it is an audio version of what seems to be a video version on the Great Courses website. You might want to check that out too.

I had a credit on my account so just picked this up. Thanks for the recommendation – seems very interesting.

Thanks for the recommendation. Plan to use a remaining credit to buy this course.

The cryptography course taught at Stanford I have found to be excellent. it really helped me gain an understanding of what I MAC's were, cbc encryption, common problems with encryption schemes, etc. after taking the course I was able to find a bug in our company's software that they didn't know about or that they didn't know was a bug.

I agree--I very strongly recommend this course. I binged Dan Boneh's lectures on a late Friday night like I was watching Netflix. I'm serious.

Did you actually complete all assignments?

No, why?

Experimentation for Improvement [0], taught by Kevin Dunn [1][2]. It's not very difficult to follow, and teaches some basics of experiment design. It's explained in a very well suited manner for non-academics, with examples about how you can implement this kind of experiments to improve things at home or at work.

[0] https://www.coursera.org/learn/experimentation [1] https://learnche.org/ [2] https://github.com/kgdunn

I would recommend Andrew Ng's updated course on Deep Learning with python instead. https://www.coursera.org/specializations/deep-learning

Yeah - the updated version is much better (I've completed both of them) just because you don't need to struggle with Matlab.

Overall, this course is extremely good mostly because Ng covers the essential theoretical topics and gives some practical advice. Also, the topics are explained really well and you do not need to look up additional material. Also, I really appreciate that he took the time to derive those equations while others just drop the results.

I'm started the Deep Learning course last night and I too think it's really good. After you finished the series of courses, what did you move on to?


As someone that's new to ML but interested in it, do you recommend people skip the original course? Does it cover the same things?

I took it about 6-7 years ago, so I totally believe you

not that good of a course, from someone who already did it

It's the course that launched Coursera (formerly ml-class.org), and is still one of the most highly rated on Coursera, so I dare say that you are in the minority with that opinion.

Care to explain why? Anyone else who did it care to chime in?

I took it several years back and enjoyed it. I liked that the course had you implement the whole training pipeline yourself rather than using a framework (not sure if the newer class does the same). While you would likely not do this in practice, I felt it helped my intuition when using the frameworks since I had a sense of how the internals were working.

It was good like 10 years ago and it did age well but it's a little bit outdated on the video/audio quality and the tools and algorithms you learn about. I think it's surprisingly up to date for a fundamentals course that old, but still a bit outdated.

I'm doing it along with Ng's newer courses at the moment and I really like that he focuses on all the basics mathematically as well and not only conceptually which gives you a deeper understanding machine learning imo. However as others have said, the audio quality is subpar and personally I find it hard to motivate myself for the programming challenges in Octave. So my suggestion would be to just view the videos and take notes and then do the newer courses and their challenges.

Due to the low quality of the video and audio I honestly struggled to want to go through the material.

I second that - great content, but terrible audio / video quality, unfortunately, which makes it less enjoyable and a bit of a struggle tbh.

It requires Matlab for instance.

I completed the course with Octave, but yeah this language is a hurdle that people don't need.

back then, matlab was the thing

actually, Matlab is still the thing depending on the domain you are working with. I don't get the hate towards Matlab generally from CS people. Maybe because it's paid?

Rather it is because it is a poorly suited language, in that isn't aware of modern programming approaches.

I took it a few years back. It was an introduction to ML course.

I'm currently taking the course "Audio Signal Processing for Music Applications", a joint course from the Pompeu Fabra University in Barcelona and Stanford, taught mainly by one of the leading figures in music technology, Xavier Serra.

I think the pacing is great even for people who are not yet into DSP; every lecture teaches fundamental concepts that build on top of each other, and many insightful examples are given (listening to waveforms, looking at spectrograms). I'm now in week 5 (I just watch the lectures at my own pace, e.g. so far I don't need the programming part of the course), and I've already learned a lot.

I personally found this to be a great one.


Having even passing familiarity with a way to think about helping people in crisis is extremely useful when you're in the moment.

Now that looks like an interesting recommendation! It looks like it can offer information I can’t find elsewhere, unlike most of Coursera’s other classes

I am taking the Modelling series(https://www.coursera.org/learn/basic-modeling) and Discrete optimization(https://www.coursera.org/learn/discrete-optimization). Great way to get your feet wet in the world of NP-hard problems.

Discrete optimization is the best course for me. It's really challenging one. I spend about a month for getting A score for all tasks - but it's rewarding experience.

I miss professor Pascal. I hope he will create a second course!

I took watched some of the lectures and did a few exercises when I had a similar course at University, it was great indeed.

Robert Sedgewick's courses, including Algorithms Part 1 and Part 2.


Can't recommend enough. I am currently doing this and loving it.

I really enjoyed the projects in these.

Coursera is great but you're going to miss a lot of opportunities by limiting yourself to just that platform.

For instance, it hasn't been widely advertised, but you can essentially take Steven Pinker's 2020 course at Harvard on Rationale: https://stevenpinker.com/classes/rationality-gened-1066

There's upwards of 20 hours of video on this course alone. You don't get that kind of depth from most Coursera MOOCs. Further, the syllabus helps narrow down a vast subject to a few months of effort and there is no better design for learning it.

Critical reasoning skills are essential! Why not learn from one of the great thinkers on the subject?

This course has the potential of ascending to the upper echelon of MOOCs. I really hope that the content doesn't get taken down. It doesn't seem downloadable..

Seeing as how the course is hosted on a proprietary video hosting service called Panopto that is a SaaS platform i think the chance of this lecture surviving for even 5 years is vanishingly small

Atleast coursera courses are easy to rip for later use...

Looks great! Is this the rationality often mentioned (and propagated) on LessWrong, Slate Star Codex and similar?

Yes it is

Loved Model Thinking, taught by Scott Page at UM. Good for the current model dependent times as well.


We live in a complex world with diverse people, firms, and governments whose behaviors aggregate to produce novel, unexpected phenomena. We see political uprisings, market crashes, and a never ending array of social trends. How do we make sense of it? Models. Evidence shows that people who think with models consistently outperform those who don't. And, moreover people who think with lots of models outperform people who use only one. Why do models make us better thinkers? Models help us to better organize information - to make sense of that fire hose or hairball of data (choose your metaphor) available on the Internet. Models improve our abilities to make accurate forecasts. They help us make better decisions and adopt more effective strategies. They even can improve our ability to design institutions and procedures. In this class, I present a starter kit of models: I start with models of tipping points. I move on to cover models explain the wisdom of crowds, models that show why some countries are rich and some are poor, and models that help unpack the strategic decisions of firm and politicians.

The models covered in this class provide a foundation for future social science classes, whether they be in economics, political science, business, or sociology. Mastering this material will give you a huge leg up in advanced courses. They also help you in life. Here's how the course will work. For each model, I present a short, easily digestible overview lecture. Then, I'll dig deeper. I'll go into the technical details of the model. Those technical lectures won't require calculus but be prepared for some algebra. For all the lectures, I'll offer some questions and we'll have quizzes and even a final exam. If you decide to do the deep dive, and take all the quizzes and the exam, you'll receive a Course Certificate. If you just decide to follow along for the introductory lectures to gain some exposure that's fine too. It's all free. And it's all here to help make you a better thinker!

Securing Digital Democracy https://www.coursera.org/learn/digital-democracy

I went through this not long after it was first offered following the 2012 elections, and it introduced me to the amazing world of security and human factors. There's more to secure systems design than just smart engineering. You have to give a lot of attention to people and priorities, and elections are a great place to see that in action.

Seconded I've taken a number of courses and this is by far the best mooc I've taken.

Cryptography I by Dan Boneh: https://www.coursera.org/learn/crypto

It's a great introduction to fundamental concepts. After you finish, I'd recommend reading this book he co-authored, which goes into more detail and covers more advanced concepts: https://toc.cryptobook.us/book.pdf

Design: Creation of Artifacts in Society is my favorite course I've ever taken:


It's taught by Karl Ulrich, a UPenn/Wharton professor/Vice Dean who helped design the Xootr scooter, Gushers, and many other awesome products. He teaches most of the course in his garage. Taking the course feels like you're his apprentice.

I really enjoyed the course on the Science of Exercise - https://www.coursera.org/learn/science-exercise.

I always wondered if there was a comprehensive way to understand how different fitness regimes and diets actually help or don't help. This course was amazing and helped my understand the fundamentals. A bit technical - goes into the basics of biology, but even without that information, this was a great course. The instructor is Dr. Robert S. Mazzeo, who has been studying, researching and teaching in the field of exercise science for over 40 years.

I have actually applied several of the principles in my workout regime and started to see the effects over the last few months. I highly recommend this one.

Not sure if I'll follow through on this but this was a great recommendation as I had no clue of its existence. Thank you very much for taking the time to recommend this and exercise science is a budding interest for me.

I wrote this course, an introduction to using the command line: https://www.coursera.org/learn/unix

Oh! Speaking of command line and the basics, this one from MIT is amazing. Covers all the basics.

The Missing Semester of Your CS Education - https://missing.csail.mit.edu/

Contents: Course overview + the shell, Shell Tools and Scripting Editors (Vim), Data Wrangling, Command-line Environment, Version Control (Git), Debugging and Profiling, Metaprogramming, Security and Cryptography

any chance you are going to make advanved level class as well? I covered basics a while ago, looking to work on efficiency

It was really nice. I can recommend!

Let me recommend you an udacity course instead. This is hands down the best course I've ever taken in my life:

AI for Trading https://www.udacity.com/course/ai-for-trading--nd880

Includes an introduction to finance/markets, and goes into strategies, multi-factor models, and deep learning. Great projects too!

It looked interesting - is it really $400 a month for access to the course, or is there some other way to just take the course without some kind of certificate?

I'm not sure, but I took really good notes (along with good pictures from the videos). I have them in org and html: https://github.com/smabie/udacity-AI-trading-notes

I've taken it but I wouldn't recommend it - it was pretty shallow. That guy used to have a more complete version of the course on Coursera. I believe that it became a specialization.

That was my fear. Looks like an OK course but I'd expect some pretty outstanding quality for $400 a month! That's crazy pricing (more than an ivy league credit hour am I right?).

I haven’t taken it, but what made it so great. Do you/can you apply what you’ve learnt?

Yeah, totally. If you're talking professionally, it helped me make the transition from a pure dev role at a financial firm to a quantitative front-end role. If you're talking about personal projects, it helps a lot if you want to develop your own quantitative trading strategies. I've been using https://www.quantopian.com/ to develop strategies and am currently trying to get on the board for their contest https://www.quantopian.com/contest. I'm hoping to try and get an allocation from them. They currently manage somewhere around a couple hundred millions dollars and allocate money to various algorithms that win the context, allowing you to net a percentage of profits of the strategy.

Wait, how did you make both notes in org and HTML? Can you write org mode notes and compile them to HTML? Is that it?

Or did you really write it twice?

(I've never used Emacs)

Org makes it very easy to export to multiple formats (including HTML). Definitely worth exploring, and definitely worth using emacs for :-)

University of Pennsylvania has an amazing course on single variable calculus.

Don't let the idea of doing 'basic' calculus turn you away as it is an incredibly tough course. The reason it can be so challenging and the reason I find it so incredible is that it teaches Calculus through the lenses of Taylor Series. Very different to other Calculus courses and as someone who hated my first year university maths course it's helped me really come to appreciate the beauty of it!

Here's the link to the first course of 5:


Ran through this series a number of years ago while I was trying my best to self-study my way to a CS bachelor's equivalent; highly recommended. I'd taken calculus in various forms before and always loved it but this one made me think about the material in a completely different light.

I strongly recommend Robert Ghrist's other courses as well, they're fantastic.

* Multivariable calculus (a linear algebra based approach, and a very nice intro to differential forms)

* Applied dynamical systems (ongoing, started recently)


That’s fantastic! Thanks for sharing!

I took it a while ago, and it was a ton of work, but I really liked https://www.coursera.org/specializations/probabilistic-graph...

I took it relatively recently but I have mixed feelings about it. While the topic itself is very interesting and they have some interesting exercises, the explanations in the videos are seriously lacking.

There are big jumps in reasoning and explanations so you'll need to rely on external material quite a bit (I've used Bayesian Reasoning by Barber) Also, exercises are presented in Matlab/Octave which makes them a pain to work with.

After completing the specialization, I've almost felt that you could just do a course that explains this course.

https://www.coursera.org/learn/genetics-evolution Beginners level.Dr. Noor is an excellent instructor.

Thanks! The teacher makes or breaks the course, and that is exactly why I asked for personal recommendations.

As an aside, the world of genetics (and molecular biology in general) is beyond fascinating. I remember coming home after one the 5th or 6th lecture on Cell biology and thinking "wow, take your worst spaghetti code and imagine the pasta becomes sentient --- that is us".

very well said ! I am wondering if anyone here has recommendations on where to go next after I finish this course ?

I loved 'Classical Sociological Theory'. It introduces the ideas of 8 'sociological' thinkers from Bernard Mandeville to Norbert Elias. The course uses the medium of a MOOC by providing insightful pictures and the course references interesting source material.

The course that brings me the most in terms of concepts I keep coming back to in everyday live is 'Introduction to Psychology': https://www.coursera.org/learn/introduction-psychology

I must confess I always find it quite hard to take psychology very serious, but this course does a good job at cutting to the bone of what psychology is about and refrains from making unfalsifiable statements.

EDIT: Almost forgot Astronomy: Exploring time and space: https://www.coursera.org/learn/astro. It comes with a very awesome free online book/website.

It's not on Coursera, but Caltech's Yaser Abu-Mostafa offers "Learning from Data" on his own website https://work.caltech.edu/telecourse.html and it's intermittently on EdX https://www.edx.org/course/learning-from-data-introductory-m...

This is far and away the best MOOC I've ever taken. The class is genuinely challenging. It's a real Caltech undergraduate course, and you can't get away with copy-pasting code or just keep resubmitting until you pass the grader. The course is focused on real understanding of what's going on mathematically, not just learning to use some library API.

I know that you didn't ask for this but I think this is the best course on Coursera hands down:


I've larned much more from this than from anything else on this site.

Any examples of what makes it good

Coursera has great content from Industry partners (Google Cloud, Amazon AWS, IBM etc) that teach everything you need to know for hacking in cloud. These skills are not widely taught in University, but skills are highly valued in the Tech industry. Three specializations (a collection of courses) that are hands-on and I would highly recommend 1.) https://www.coursera.org/specializations/aws-fundamentals 2.) https://www.coursera.org/specializations/gcp-data-machine-le... 3.) Anything from deeplearning.ai [disclosure: I work at Coursera]

I would start with 'Learning how to Learn' - https://www.coursera.org/learn/learning-how-to-learn

I took it a few years back. Highly recommended.

I would absolutely love to hear someone's experience taking this course: https://www.coursera.org/specializations/coding-for-managers...

I probably won't have time to sit through it, but I've taken a course from this instructor in the past and he is pretty good. I'm really curious to know how he explains coding and engineering principles to "managers, designers and entrepreneurs".

programming languages (parts a, b, and c) by dan grossman


introduces the underpinnings of programming languages via standard ml, racket, and ruby.

I second this recommendation. A really good course, and if you do all 3 parts it's really the equivalent of a full university course. Covers the main concepts in modern programming languages, such static vs. dynamic typing, OO vs functional, and does so I consdierable depth, with reasonably challenging programming projects. Even if you think you already know this stuff, it's a good review and I guarantee you'll learn a few new things.

Also, Dan is a good professor and is really enthusiastic about the subject.

yep, it's a very fun course and very well organized, including code reviews for homework. i fixed some issues found in mine and enjoyed reviewing others' work. i do wish there were programming assignments for sml's module system though. that part felt a bit tacked on.

i still need to go back and finish the ruby section. at that point in the course, i got distracted with other things.

Unstuck to your categories, Jonathan Biss's course on Beethoven's sonatas is fantastic.


Two courses taught by faculty at the Russian institute HSE (Higher School of Economics).

1. How to Win a Data Science Competition https://www.coursera.org/learn/competitive-data-science

2. Bayesian Methods for Machine Learning https://www.coursera.org/learn/bayesian-methods-in-machine-l...

Had been working on https://www.tutorack.com/ for a while - aggregates online resources (not just MOOCs). Haven't updated it in sometime but you should find something useful

The first 3 courses of the Statistics with R specialization, taught by Dr. Mine.


I really liked the approximation algorithms from a french university

Ok but do you need a course meant as a vocational, subject-specific training or something more liberal that helps you think better for the long run? I would go for the latter.

most of the courses on coursera are for uninitiated and have very shallow content (there are some rare exception). So if you like you can search for a good book or some video lecture from good university. like this is for statistical learning "https://online.stanford.edu/courses/sohs-ystatslearning-stat...

Broken link due to quote marks. Great suggestion otherwise.


I think you are generalising too much, there are also some good courses on Coursera. But many are more shallow than e.g. EdX, which has some excellent more in depth courses.

That's true. I think most of the recent courses from John Hopkins university illustrate this the best. They even have a separate course for Linear Regression!

However, if you spend enough time sifting through the junk, there are some decent material.

I’ve started working through the Verification series by EIT which have been challenging and interesting. If you’re interested in formal verification, check them out.

Can you please post a link to this?

Princeton’s Computer Architecture course is great.

Dan Boneh's Crypto II. ;-)

epidemics, learning how to learn, algorithms 1 & 2

> bioinformatics

The Honors Track of the UCSD series is really great.


It's super hard and as a side effect you learn a ton about very interesting, amazing, and useful algorithms that you'd never even hear about in a top notch CS program.

That sounds great! I'll check it out tomorrow.

Some people here in Europe take bioinformatics as a shorthand of "database management, pipeline construction, and scaffold building" --- I'm glad to see the course is more algorithm oriented (maybe with a bit of DS thrown in as well).

Surprised this is buried so deep. These courses are excellent.

I recently took a Coursera course on Schizophrenia and found it fascinating. YMMV of course.

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